1 Loading and processing of data


1.1 Count matrix

First, we load and filter the count matrix.

1.2 Check distribution

Log transformation increases normal distribution of sample measurements for the respective metabolites

2 Dimensionality reduction methods


2.1 PCA

PCAPCA

PCA

3 Clustering and correlation between sample


Next, we perform clustering and correlation analysis, and plot these results in a couple of heatmaps. Most of the samples looked fine, no sample was removed as outlier.

3.1 Distance matrix, followed by clustering

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Heatmap of distances

Heatmap of distances

3.2 Correlation between samples

Heatmap of correlations

Heatmap of correlations

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3.3 Hierarchical clustering of the samples

Hierarchical clustering

Hierarchical clustering

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4 Differential abundance analysis


We performed a differential abundance analysis by parametric testing (two-sided t-test). The mean value of replicates per patient-derived cell line was used.

4.1 Average values of replicates per patient-derived cell line

4.2 Differential abundance analysis using two-sided t-test

And these are the results of our differential abundance analysis. Only genes with a signficant adjusted p-value are shown.

5 Visualization of significantly altered metabolites

5.1 Draw boxplots of significantly altered metabolites

5.2 Vulcano-plot

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